aws meetup - nordstrom data lab and the aws cloud
DESCRIPTION
The Nordstrom Data Lab is building out an API that powers product recommendations for our customer online and beyond. Recommendo, our flagship product, was built from the ground up using Node.js and AWS in a little over three months. Since launch in November 2013 we've served up over three billion recommendations and survived Black Friday and Cyber Monday without breaking a sweat. We'll be sharing our learnings for building and operating a high traffic API on the AWS platform as a service focusing on Node.js, Elastic Beanstalk, and DynamoDB. Additionally we'll discuss some of the cultural challenges and opportunities presented when adopting the public cloud at a large corporate IT organization. In short, we believe there are tremendous advantages to be had for enterprises willing to make the leap to the cloud.TRANSCRIPT
Jason Wilson & David Von LehmanPRESENTING
AWS and the Nordstrom Data Lab
Recommendo Overview
• REST-ful product recommendations API • Live on nordstrom.com in November• Service emails live in January• Lives in the AWS cloud – Elastic Beanstalk,
DynamoDB, node.js• 3rd party rec vendors don’t tap into what is
unique about Nordstrom or fashion
By the Numbers
• Over 4 billion recommendations served• >3 million API hits per day• 105 days between first commit and go-live (Aug 6
and Nov 19 respectively)• 5 servers with auto-scaling to 20 (turns out we don’t
need them)• 90ms average request latency
50/50 test againstincumbent vendor
How We Built It
• Continuous integration and deployment from the first week
• 90+ percent code coverage• Fewer moving parts == less to monitor, fewer
ways for things to go wrong• Fully PaaS based to minimize sys admin
responsibilities• How can we support this ourselves without
carrying pagers?
DynamoDB
• Fully managed NoSQL database-as-a-service• Web API with SDK support for Python, Ruby,
node.js, .NET, and Java• High performance queries, backed by SSD• Maintains predictable performance for data at any
size through horizontal scale out• Auto replication across 3 availability zones• Need to understand data access patterns up front• Pay for only what you use/need – both storage and
R/W throughput
• JavaScript on the server atop the Google V8 engine• Asynchronous event loop makes it ideal for real-time
data intensive applications• Vibrant open-source community around excellent
npm package manager (50K+ packages)• Seeing increased adoption in enterprises including
Wal-Mart, LinkedIn, PayPal, Dow Jones, Microsoft, New York Times
JavaScript – Learn to Love It
• No type checking, don’t find errors until runtime
• Not classical OO• var keyword • Callback hell• Server debugging too hard• But wait..
• Chrome and V8• Dynamic can be your friend• npm! • express, async, mocha
AWS Components
• EC2 – Provides web-scale computing as a service.
• ELB – elastic load balancer. Routes incoming traffic to ec2 instances, scales up to meet demand.
• Auto-scaling group – a logical collection of EC2 instances behind an ELB
AWS Components
Elastic Beanstalk
• AWS PaaS – lightweight abstraction layer atop EC2/ELB with no additional costs
• More transparent than Azure or Heroku• Supports Java, .NET, Python, Node.js, PHP, and Ruby• git push deployment• Auto-scaling group with custom triggers and auto applied
config• Possible to configure the AMI including yum packages,
environment variables, and more• Supports custom AMIs• Automated health checks
Continuous Deployment
git push to dev branch
Jenkins CI
unit tests
git push to EB
git pull dev
git checkout master
git merge dev
git push master
Jenkins CI
unit tests
git push to EB (prod)
DevelopmentProduction
Performance testing
• Initial performance was poor.• Disable DNS caching when load testing against
ELB. • Pre-warm ELB for higher upfront throughput• jmeter-ec2, bees with machine guns
Early Perf results – YIKES!Transactions per second
Response time (seconds)
Performance tuning
• New relic, Nodetime– Real-time performance monitoring of node
runtime• node-mem-watch
– Evented inspection of heap, gc events, leak events, and heap diffing
• ssh into instances
Real Performance
• Pleasantly surprised • Average latency ~90ms• Dynamo response times <10ms• Handful of auto-scaling up and back events• One outage due to bad exception handling
400%
64%
DynamoDB
Lessons Learned / Pitfalls
• True zero downtime deployment is difficult to achieve
• Thoroughly explore the Elastic Beanstalk configuration options
• Catch those errors – a rogue unhandled exception can bring it all down
• Health checks that actually do something• Out of the box monitoring is pretty good
Harness the Cloud
On-Premise IaaS PaaS
% time
infrastructure experience
Logging Monitoring
Redundancy
DeploymentAutomation
High-Availability
Scalability
Iterative Development
Build to Experiment
EvolutionaryArchitecture
Change Tolerant
FrequentReleases
Small TeamsDo Both!
Agility vs. Industrial Strength
Security
PaaS Venn Diagram
Robust Systems
Rapid Delivery
Platypus as a Service
Recommendo 2.0
• Sku based recommendations – size!• Truly personalized recs based on individual browse
and purchase history
DynamoDBBatchRecs
Real-Time
Refinery
Scorer Ingester Redis
Streams
Additional AWS Services
• Elasticache and Redis• Elastic Beanstalk worker tiers• SQS• S3
Wrap-Up
• Recommendo – initial success, now building upon what we have learned
• Node.js + DynamoDB + Elastic Beanstalk is a winning combination
• Possible to out-perform an incumbent vendor solution in a competitive differentiating capability
• Cloud and PaaS enable small teams to move quick and deliver solid production caliber systems
• Incremental cost of “gold plating” steadily shrinking• Your company benefits when percent of resources devoted to
core competency is maximized
